Layered masking of content

Abstract

Methods, systems and computer program products for layered masking of data are described. A system receives content including personally identifiable information (PII). The system redacts the content by masking the PII. The system identifies the PII in multi-layer processing, where in each layer, the system determines a respective confidence score indicating a probability that a token is PII. If the confidence score is sufficiently high, the system masks the token. Otherwise, the system provides the token to a next layer for processing. The layers can include regular expression based processing, lookup table based processing, and machine learning based processing.

What is claimed is: 
     
       1. A method comprising:
 receiving content including a token; 
 storing one or more regular expressions for the content, wherein each regular expression comprises a sequence of symbols and characters expressing a string or pattern; 
 determining, by a computer system based on matching the one or more regular expressions with the token, a first confidence score indicating a probability that the token includes personally identifiable information (PII), the first confidence score being associated with the regular expression; 
 storing a lookup table that includes one or more tokens for known PII; 
 determining, by the computer system based on matching the token with tokens in the lookup table, a second confidence score indicating a probability that the token includes PII, the second confidence score being associated with a term in the lookup table that is an exact match of the token; 
 storing a model for determining a third confidence score indicating a probability that the token includes PII, wherein the model is generated using a machine learning training algorithm; 
 determining, by the computer system based on inputting the token into the model, the third confidence score; 
 masking the token by the computer system based on the first confidence score, the second confidence score and the third confidence score; and 
 providing, by the computer system as data of improved privacy, the content including the masked token to a content consuming device. 
 
     
     
       2. The method of  claim 1 , wherein:
 the content includes transaction data, the transaction data including one or more transaction records each corresponding to a respective transaction and each including a description of the transaction, and 
 the token is a part of the description and includes at least one of a number or a word. 
 
     
     
       3. The method of  claim 1 , wherein the one or more regular expressions are derived from historical transaction data, the first confidence score is derived from a ratio between a number of terms in the historical transaction data that match the regular expression and that are PII and a total number of terms in the historical transaction data that match the regular expression. 
     
     
       4. The method of  claim 1 , wherein the lookup table is derived from historical transaction data, the second confidence score is derived from a ratio between a number of appearances of the token in the historical transaction data as PII and a total number of appearances of the token in the historical transaction data. 
     
     
       5. The method of  claim 1 , wherein the model is based on a conditional random field (CRF) algorithm. 
     
     
       6. The method of  claim 5 , comprising providing training data for the CRF algorithm, the training data including first data marked as PII and second data marked as non-PII, wherein the first data is labeled as noun forms for the CRF algorithm, the second data is labeled as non-noun forms. 
     
     
       7. The method of  claim 1 , wherein masking the token based on the first confidence score, the second confidence score and the third confidence score comprises:
 determining that at least one of the first confidence score, the second confidence score or the third confidence score satisfies a respective confidence threshold; and 
 in response to the determining, masking the token. 
 
     
     
       8. The method of  claim 1 , wherein masking the token based on the first confidence score, the second confidence score and the third confidence score comprises:
 determining that none of the first confidence score, the second confidence score or the third confidence score satisfies a respective confidence threshold; 
 determining whether a weighted combination of the first confidence score, the second confidence score and the third confidence score satisfies a combined confidence threshold; and 
 masking the token upon determining that the weighted combination satisfies the combined confidence threshold. 
 
     
     
       9. The method of  claim 1 , wherein the masking the content is performed in a micro batching mode, wherein the content is divided into a plurality of sets of strings, each set having a size limit, and the masking is performed on each set. 
     
     
       10. The method of  claim 1 , wherein the masking the content is performed in a batch mode or a real time mode. 
     
     
       11. A system comprising:
 one or more processors; and 
 a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
 receiving content including a token; 
 storing one or more regular expressions for the content, wherein each regular expression comprises a sequence of symbols and characters expressing a string or pattern; 
 determining, based on matching the one or more regular expressions with the token, a first confidence score indicating a probability that the token includes personally identifiable information (PII), the first confidence score being associated with the regular expression; 
 storing a lookup table that includes one or more tokens for known PII; 
 determining, based on matching the token with tokens in the lookup table, a second confidence score indicating a probability that the token includes PII, the second confidence score being associated with a term in the lookup table that is an exact match of the token; 
 storing a model for determining a third confidence score indicating a probability that the token includes PII, wherein the model is generated using a machine learning training algorithm; 
 determining, based on inputting the token into the model, the third confidence score; 
 masking the token based on the first confidence score, the second confidence score and the third confidence score; and 
 providing, as data of improved privacy, the content including the masked token to a content consuming device. 
 
 
     
     
       12. The system of  claim 11 , wherein:
 the content includes transaction data, the transaction data including one or more transaction records each corresponding to a respective transaction and each including a description of the transaction, and 
 the token is a part of the description and includes at least one of a number or a word. 
 
     
     
       13. The system of  claim 11 , wherein the one or more regular expressions are derived from historical transaction data, the first confidence score is derived from a ratio between a number of terms in the historical transaction data that match the regular expression and that are PII and a total number of terms in the historical transaction data that match the regular expression. 
     
     
       14. The system of  claim 11 , wherein the lookup table is derived from historical transaction data, the second confidence score is derived from a ratio between a number of appearances of the token in the historical transaction data as PII and a total number of appearances of the token in the historical transaction data. 
     
     
       15. The system of  claim 11 , wherein the model is based on a conditional random field (CRF) algorithm. 
     
     
       16. The system of  claim 15 , comprising providing training data for the CRF algorithm, the training data including first data marked as PII and second data marked as non-PII, wherein the first data is labeled as noun forms for the CRF algorithm, the second data is labeled as non-noun forms. 
     
     
       17. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
 receiving content including a token; 
 storing one or more regular expressions for the content, wherein each regular expression comprises a sequence of symbols and characters expressing a string or pattern; 
 determining, based on matching the one or more regular expressions with the token, a first confidence score indicating a probability that the token includes personally identifiable information (PII), the first confidence score being associated with the regular expression; 
 storing a lookup table that includes one or more tokens for known PII; 
 determining, based on matching the token with tokens in the lookup table, a second confidence score indicating a probability that the token includes PII, the second confidence score being associated with a term in the lookup table that is an exact match of the token; 
 storing a model for determining a third confidence score indicating a probability that the token includes PII, wherein the model is generated using a machine learning training algorithm; 
 determining, based on inputting the token into the model, a third confidence score; 
 masking the token based on the first confidence score, the second confidence score and the third confidence score; and 
 providing, as data of improved privacy, the content including the masked token to a content consuming device. 
 
     
     
       18. The non-transitory computer-readable medium of  claim 17 , wherein the lookup table is derived from historical transaction data, the second confidence score is derived from a ratio between a number of appearances of the token in the historical transaction data as PII and a total number of appearances of the token in the historical transaction data. 
     
     
       19. The non-transitory computer-readable medium of  claim 17 , wherein masking the token based on the first confidence score, the second confidence score and the third confidence score comprises:
 determining that at least one of the first confidence score, the second confidence score or the third confidence score satisfies a respective confidence threshold; and 
 in response to the determining, masking the token. 
 
     
     
       20. The non-transitory computer-readable medium of  claim 17 , wherein masking the token based on the first confidence score, the second confidence score and the third confidence score comprises:
 determining that none of the first confidence score, the second confidence score or the third confidence score satisfies a respective confidence threshold; 
 determining whether a weighted combination of the first confidence score, the second confidence score and the third confidence score satisfies a combined confidence threshold; and 
 masking the token upon determining that the weighted combination satisfies the combined confidence threshold.